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  <div class="award">
    <a href="http://www.acmmm10.org/2010/10/open-source-software-competition-winners/"
    style="text-decoration:none">ACM OpenSource Award 2010</a>
  </div>

  <p>The <b>VLFeat</b> <a
  href="%pathto:license;">open
  source</a> library implements popular computer vision algorithms
  including
  <em>SIFT</em>, <em>MSER</em>, <em>k-means</em>, <em>hierarchical
  k-means</em>, <em>agglomerative information bottleneck, and quick
  shift</em>.  It is written in C for efficiency and compatibility,
  with interfaces in MATLAB for ease of use, and detailed
  documentation throughout. It supports
  <em>Windows</em>, <em>Mac OS X</em>, and <em>Linux</em>. The latest version of
  VLFeat is <code>%env:VERSION;</code>.</p>

 <table class="boxes">
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 <td style="height:6em;padding-right:0.5em;padding-bottom:0.5em;">
 <div class="box">
  <h1>Download</h1>
  <ul>
   <li><b>
       <a href="%pathto:root;download/vlfeat-%env:VERSION;-bin.tar.gz"
          onClick="javascript:pageTracker._trackPageview('/download/vlfeat-%env:VERSION;-bin.tar.gz');">VLFeat
   %env:VERSION;</a></b> (Windows, Mac, Linux)</li>
   <li><a href="%pathto:download;">Source code and installation</a></li>
   <li><a href="http://github.com/vlfeat/vlfeat/tree/master">Git
   repository</a></li>
  </ul>
 </div></td>
 <td style="height:6em;padding-left:0.5em;padding-bottom:0.5em;">
 <div class="box">
  <h1>Documentation</h1>
  <ul>
   <li><a href="%pathto:mdoc;">MATLAB commands</a></li>
   <li><a href="%pathto:api;">C API</a> with algorithm descriptions</li>
   <li><a href="%pathto:man;">Command line tools</a></li>
  </ul>
  </div>
 </td>
 </tr>
 <tr>
 <td style="height:15em;padding-right:0.5em;padding-top:0.5em;">
 <div class="box">
  <h1>Tutorials</h1>
  <ul>
  <li>Features:  <a href="%pathto:tut.sift;">SIFT</a>, <a href="%pathto:tut.mser;">MSER</a>,
    <a href="%pathto:tut.qs;">Quick shift</a>, <a href="%pathto:tut.slic;">SLIC</a></li>
  <li>Clustering: <a href="%pathto:tut.ikm;">IKM</a>, <a href="%pathto:tut.hikm;">HIKM</a>,
    <a href="%pathto:tut.aib;">AIB</a></li>
  <li>Matching: <a href="%pathto:tut.kdtree;">Randomized kd-trees</a></li>
  <li><a href="%pathto:tut;">All tutorials</a></li>
  </ul>
  <h1>Example applications</h1>
  <ul>
  <li><a href="%pathto:apps.caltech-101;">Caltech-101 classification</a></li>
  <li><a href="%pathto:apps.sift-mosaic;">SIFT matching for
  auto-stitching</a></li>
  <li><a href="%pathto:apps;">All applications</a></li>
  </ul>
 </div></td>
 <td style="height:15em;padding-left:0.5em;padding-top:0.5em;">
 <div class="box">
  <h1>Citing</h1>
<pre style="font-size: .8em; color: black; margin-top: 0.5em ; margin-bottom: 1.1em; line-height:1.1em;">
@misc{vedaldi08vlfeat,
 Author = {A. Vedaldi and B. Fulkerson},
 Title = {{VLFeat}: An Open and Portable Library
          of Computer Vision Algorithms},
 Year  = {2008},
 Howpublished = {\url{http://www.vlfeat.org/}}
</pre>
<h1>Acknowledgments</h1>
<p><a href="http://vision.ucla.edu">UCLA Vision
Lab</a>, <a href="http://www.robots.ox.ac.uk/~vgg/">Oxford VGG</a>.</p>
</div></td></tr></table>

 <h2 style="clear:left;">News</h2>

 <div class="clear">&nsbp;</div>
 <dl id="changes">
   <dt><span class="date">24/12/2011</span> VLFeat 0.9.14 released</dt>
   <dd>VLFeat 0.9.14 adds SLIC superpixels, improves the
     documentation, and contains other improvements and
     bugfixes. Furthermore, starting from this release VLFeat is
     distributed under the <a href="%pathto:license;">BSD license</a>.
     [<a href="http://github.com/vlfeat/vlfeat/commits/v0.9.14">Details</a>].
   </dd>
   <dt><span class="date">12/7/2011</span> VLFeat 0.9.13 released</dt>
   <dd>VLFeat 0.9.13 fixes the Windows binary package.
     [<a href="http://github.com/vlfeat/vlfeat/commits/v0.9.13">Details</a>].
   </dd>
   <dt><span class="date">5/7/2011</span> VLFeat 0.9.12 released</dt>
   <dd>VLFeat 0.9.12 contains minor bugfixes.
     [<a href="http://github.com/vlfeat/vlfeat/commits/v0.9.12">Details</a>].
   </dd>
   <dt><span class="date">19/6/2011</span> VLFeat 0.9.11 released</dt>
   <dd>VLFeat 0.9.11 solves a compatibility issue with old versions of
     Mac OS X and brings other minor bug fixes as well.
     [<a href="http://github.com/vlfeat/vlfeat/commits/v0.9.11">Details</a>].
   </dd>
   <dt><span class="date">11/6/2011</span> VLFeat 0.9.10 released</dt>
   <dd>VLFeat 0.9.10 rolls out numerous bug fixes
     and improvements, especially to the
     homogeneous kernel map implementation.
     [<a href="http://github.com/vlfeat/vlfeat/commits/v0.9.10">Details</a>].
   </dd>
   <dt><span class="date">28/10/2010</span> VLFeat wins the ACM Multimedia Open Source Awards!</dt>
   <dd>VLFeat 0.9.9 was awarded the <a href="http://www.acmmm10.org/2010/10/open-source-software-competition-winners/">ACM Multimedia Open Source Award 2010</a>.
   </dd>
   <dt><span class="date">14/6/2010</span> VLFeat 0.9.9 released</dt>
   <dd>VLFeat 0.9.9 adds a new sample application (SIFT matching) and
     minor refinements.
     [<a href="http://github.com/vlfeat/vlfeat/commits/v0.9.9">Details</a>].
   </dd>
   <dt><span class="date">14/6/2010</span> Open Source Vision Software Tutorial</dt>
   <dd>VLFeat presented at the CVPR 2010 Open Source Vision Software
Tutorial. Slides of the presentation are available from
the <a href="http://www.vlfeat.org/cvpr10">tutorial web page</a>.</dd>
   <dt><span class="date">10/5/2010</span> VLFeat 0.9.8 released</dt>
   <dd>VLFeat 0.9.8 adds new tutorials, (hierarchical) k-means support for
     floating point data, homogeneous kernel maps, a basic implementation
     of PEGASOS for SVM learning, and many other improvements.
     [<a href="http://github.com/vlfeat/vlfeat/commits/v0.9.8">Details</a>].
   </dd>
   <!--
   <dt><span class="date">16/01/2010</span> VLFeat 0.9.7 released</dt>
   <dd>VLFeat 0.9.7 updates the binary distribution to be backward
     compatible with Mac OS X 10.5 (Leopard).
     [<a href="http://github.com/vlfeat/vlfeat/commits/v0.9.7">Details</a>].
   </dd>
   <dt><span class="date">10/01/2010</span> VLFeat 0.9.6 released</dt>
   <dd>VLFeat 0.9.6 contains minor improvements to the binary
   distribution. Specifically, it makes VLFeat GNU/Linux distribution
   compatible with the older GLIBC version 2.3.
   [<a href="http://github.com/vlfeat/vlfeat/commits/v0.9.6">Details</a>].
   </dd>
   <dt><span class="date">30/11/2009</span> VLFeat 0.9.5 released</dt>
   <dd>VLFeat 0.9.5 adds a fast kd-tree implementation and
   SSE-acelerated vector/histogram comparison. The dense SIFT (dsift)
   implementation has also been improved. Binaries and compilation
   support for Mac OS 10.6 (Snow Leopard) and MATLAB R2009b (32 and 64
   bit) have been added
   [<a href="http://github.com/vlfeat/vlfeat/commits/v0.9.5">Details</a>].
   <blockquote>
     <b>MATLAB 7.0</b> and earlier require recompling the MEX files by
     the provided <code>vl_compile</code> command.
   </blockquote>
   </dd>
   -->
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